Quantitative biometry of zebrafish retinal vasculature

using optical coherence tomographic angiography

by

Ivan Bozic

Thesis

Submitted to the Faculty of the

Graduate School of Vanderbilt University

in partial fulfillment of the requirements

for the degree, of

MASTER OF SCIENCE

in

Biomedical Engineering

May 11, 2018

Nashville, Tennessee

Approved:

Yuankai K. Tao, PhD

Justin Baba, Ph.D.

ii

TABLE OF CONTENTS

Page

LIST OF FIGURES ....................................................................................................................... iii

Introduction ......................................................................................................................................1

Methods............................................................................................................................................3

Imaging system .............................................................................................................................3

Imaging protocol ...........................................................................................................................3

Vessel segmentation and labeling .................................................................................................4

Dataset acquisition and processing .........................................................................................5

Skeletonization of vasculature maps .......................................................................................5

Vessel classification and branch point detection ....................................................................5

Quantitative vascular biometry ...............................................................................................6

Retinal vascular biometry for animal identification .....................................................................6

Results ..............................................................................................................................................8

Discussion and Conclusion ............................................................................................................14

REFERENCES ..............................................................................................................................15

iii

LIST OF FIGURES

Figure Page

1. Imaging system ................................................................................................................3

2. Processing algorithm ........................................................................................................4

3. Branch kernel matrix .......................................................................................................5

4. Quantitative vascular biometry ........................................................................................6

5. Identification schematic ...................................................................................................7

6. In vivo retinal OCT in zebrafish .......................................................................................8

7. In vivo retinal OCT-A in zebrafish ..................................................................................8

8. Segmentation errors at the FOV periphery ......................................................................9

9. Segmentation errors at the ONH ....................................................................................10

10. Quantitative vascular biometry ......................................................................................11

11. Vessel branch length comparison between longitudinal timepoints ..............................12

12. First generation length differences between eyes ..........................................................13

1

INTRODUCTION

As of 2010, there were an estimated 285 million visually impaired and 39 million blind individuals

worldwide [1]. In the United States, proliferative diabetic retinopathy (PDR) and wet age-related

macular degeneration (AMD) are two of the leading causes of severe vision-loss and blindness [1,

2]. PDR is a complication of diabetes that is characterized by neovascularization originating from

the retina and optic disc, that can result in hemorrhage, fibrosis, traction, and retinal detachment

[3, 4]. An estimated 415 million adults suffer from diabetes globally, and almost half of all

diabetics are expected to experience some degree of retinopathy [4, 5]. AMD is the leading cause

of blindness among people aged 55 and older in the developed world and affect more than 1.75

million individuals in the United States [6, 7]. Wet AMD is characterized by neovascularization,

that often leads to hemorrhage and exudation. While only 10 percent of AMD patients develop

wet AMD, severe vision-loss progresses quickly in the majority of these patients [6].

Vascular endothelial growth factor (VEGF) inhibitors have become standard treatments for

both PDR and wet AMD [8, 9]. Intravitreal anti-VEGF injection has been shown to significantly

stabilize visual acuity (VA), with 91.5-95.4% of wet AMD patients in one clinical study showing

less than a 15-letter decrease in VA [10]. However, anti-VEGF treatment has been suggested to be

less effective at maintaining VA, and one study showed that 34% of patients experienced a VA

loss of >15 letters after five years of treatment [11]. One major limitation of anti-VEGF therapy is

the need for repeated injections on either a monthly or as-needed basis (~6-7 injections per year)

[4] [10, 13]. In addition to patient anxiety, repeat injections also increase the risk of side effects

such as endophthalmitis [15]; acute increases in intraocular pressure requiring topical or surgical

anti-glaucoma interventions [16]; and off-target drug effects including loss of retinal ganglion

cells and circulation disturbances in the choriocapillaris [9]. The aforementioned limitations of the

current clinical standard-of-care and a lack of understanding of the structural, metabolic, and

vascular changes underlying retinal neovascularization highlights the need to identify mechanisms

of pathogenesis and novel anti-angiogenic therapies.

The zebrafish (Danio rerio) is a popular model organism because its fecundity and life cycle

have enabled development of mutant phenotypes of human pathologies and they are well-suited

for large scale experiments [17, 18]. As an ophthalmological model, the zebrafish retina shares

similar structure and function with that of humans and other vertebrates [22]. Similar to humans,

the zebrafish retina is composed of seven major cell types (six neural and Müller glial cells), three

nuclear layers separated by two plexiform layers, and a highly ordered mosaic organization of

neurons in each layer [23, 24]. As diurnal species, humans and zebrafish have cone-dominant

vision, in contrast to the rod-dominant vision found in mice; this has advantages for studying cone

degeneration diseases such as AMD [24, 25].

Zebrafish readily absorb compounds from their aqueous environment and are also affected byt

them, which allows for induction of pathologies and delivery of chemical compounds without the

need for injections [21]. Retinal vasculopathies can be modeled by exposing animals to hypoxic

water for 3-10 days to induce neovascularization and vascular leakage. Similarly, exposure to

glucose induces hyperglycemia, which has been shown to result in retinal structural abnormalities

similar to those in DR [25]. As a pharmacological model, 82% of disease-causing human proteins

2

have orthologues in zebrafish, and pharmacological effects are highly conserved between human

and zebrafish [26][27][28].

The majority of drug screens in zebrafish are performed using larval animals because their

transparency and size are well-suited for imaging and housing in large-scale studies [29]. However,

normal vascular development [30] and differences in inflammatory and immune responses

between larva and adults [30] may confound structural and functional changes in larval zebrafish

models of DR. In this study, we demonstrate in vivo retinal imaging in adult zebrafish (≥3 months

post-fertilization, mpf) using optical coherence tomography (OCT) [31] and OCT angiography

(OCT-A) [32] and present post-processing algorithms for vascular segmentation and biometry.

Quantitative measurements of retinal perfusion and angiogenesis during longitudinal studies can

provide insights into disease pathogenesis and therapeutic efficacy in drug screens for novel anti-

angiogenic compounds. In addition to tracking functional changes, retinal vascular biometry can

also be used as a method for uniquely identifying individual animals with high sensitivity and

specificity. To this end, we developed and validated a novel identification method that obviates

the need for physical marking methods such as elastomer marking, freeze branding, removal of

specific scales, fin clipping, and dorsal fin tagging [33]. We believe the retinal vascular biometry

methods presented here are robust enabling technology that will broadly benefit large-scale

zebrafish studies.

3

METHODS

Imaging system

OCT and OCT-A imaging of wild-type zebrafish was performed using a custom-built spectral

domain OCT (SD-OCT) system (Fig. 1(a)). A superluminescent diode (InPhenix) with 855 nm

central wavelength and 90 nm bandwidth was split between reference and sample arms using an

80:20 coupler, respectively. The intensity was detected using the central 2048 pixels of a 4096

pixels linear CMOS array with 125 kHz line-rate (spL4096-140km, Basler). Measured system

SNR was 107 dB with -6 dB falloff at 1.1 mm and 2.56 µm axial resolution in tissue. Zebrafish

were imaged using a 1 mm diameter spot size at the pupil with 700 µW of optical power.

Fig. 1. Imaging system. (a) Custom-built SD-OCT system. CMOS, detector; f,

collimating, objective, ophthalmic, and scan lenses; G, galvanometers; M, mirror;

PC, polarization controller; SLD, superluminescent diode; VPHG, grating.

Zebrafish retina were imaged in air through a contact lens and positioned using a

custom holder (inset). (b) 5-axis alignment stage.

Imaging protocol

In vivo imaging was performed under an animal protocol approved by the Institutional Animal

Care and Use Committee (IACUC) at Cleveland Clinic. Ten adult wild-type zebrafish (≥3 mpf)

were imaged repeatedly during two sessions on ten different days over four weeks. Both eyes were

imaged during each imaging session (20 total datasets per eye). Repeat imaging sessions on each

day were separated by a two-hour break and imaging days were separated by 48 hours.

Animals were anesthetized prior to imaging using a 0.14% Tricaine solution. Anesthetized

animals were positioned using a custom holder and the retina was imaged through a contact lens

(Fig. 1(b)) [41] . Zebrafish OCT and OCT-A volumetric datasets were centered on the optical

nerve head (ONH) using a 5-axis stage (Fig. 1(b), Leica Microsystems). OCT volumes consisted

of 2500 B-scans (2048 x 500 pix.) acquired in approximately 10 s. These datasets included five

repeated B-scans at each lateral position for OCT-A post-processing. Animal imaging and

handling were performed in less than 10 minutes followed by recovery, during which water was

forced across the gills to maximize animal survival for the duration of the study [41]. Between

4

imaging days, fish were housed in light exposure and temperature controlled rooms in separate

boxes and grouped so that individual animals were uniquely identifiable by their strip/spots

patterns and caudal fin cuts.

Vessel segmentation and labeling

Vessel segmentation, labeling, and feature extraction were performed on en face OCT-A

projections using custom developed algorithms (Fig. 2). The steps are grouped into four categories

to better represent the processing pipeline. Each step is described in detail in the following sections.

Fig. 2. Processing algorithm. (a) Algorithm block diagram showing a flow chart of

vessel segmentation, labeling, and feature extraction; and (b) Processing results in

each step.

5

Dataset acquisition and processing

Raw OCT B-scan bulk motions were removed using discrete Fourier transform cross-correlation

[42]. First, repeated B-scans at the same location were registered to each other. The resulting

registered B-scans were then averaged, and all of the averaged B-scans in the volumetric dataset

was registered. Volume registration parameters were calculated using OCT data and applied to the

corresponding OCT-A frames.

OCT-A vasculature maps were calculated using weighted optical microangiography (wOMAG)

[43]. wOMAG uses OCT intensities to remove OCT-A artifacts caused by tissue hyper-

reflectivity. Here, raw optical microangiography (OMAG) frames were weighted by an intensity

decorrelation function, (D/D0)n. The decorrelation coefficient, D, represents the intensity

decorrelation between repeated B-scans, and optimal values for D0 and n were experimentally

determined (D0 = 0.1, n = 0.5).

Skeletonization of vascular maps

En face OCT-A projections were lowpass filtered to smooth vessel contours and remove speckle

noise. A vertical intensity gradient was also calculated and subtracted from the filtered vessel maps

to remove breathing artifacts (horizontal streaks). The resulting OCT projections were then

binarized and skeletonized using dilatation and erosion [44]. Morphological dilatation and erosion

were performed to expand and compress the binary images. Dilated OCT-A projection was

performed using a circular kernel (5 pixels radius) for unification of the vessel thickness. This was

followed by erosion to obtain a one pixel width skeleton.

Vessel classification and branch point detection

Skeletonized vasculature maps were used to detect vessel branch points. Vessels were first

classified as either ONH or retinal vessels. The retinal pigment epithelium (RPE) was segmented

from OCT cross-sections [45] and the ONH was identified by the discontinuity in the RPE. The

resulting ONH segmentation was then fit to a circle defined by the ONH center and radius. All

vessel branches within the ONH radius were classified as ONH vessels and all remaining vessels

were further processed for branch point identification.

A set of 18 predefined 3x3 pixel branch kernels was created to represent all possible orientations

of vessel bifurcation and trifurcation (Fig 3). The spatial location of each vessel branch point was

identified by convolving the skeletonized vasculature map with each kernel. Branch points were

then used to classify each vessel segment by branch generation relative to the ONH (Fig. 4).

Fig. 3. Branch kernel matrix. 18 predefined 3x3 pixel kernels were created to

represent all possible vessel branch orientations.

6

Quantitative vascular biometry

Quantitative biometry was performed by extracting vessel segment length, curvature, and branch

angle between branch points in the skeletonized vessel map (Fig. 4). Segment length was defined

as the total number of pixels in each vessel segment, curvature was calculated as the ratio between

the vessel segment length and Euclidean distance between corresponding branch points, and angle

was calculated as the angle between vessel segments. Vascular biometrics were extracted for each

vessel branch originating from the ONH beginning at the 12 o’clock position and moving

clockwise around the ONH.

Fig. 4. Quantitative vascular biometry. Angle, curvature, and length were defined

on skeletonized vascular maps. All branch generations are referenced with respect

to their connectivity relative to the ONH.

Retinal vascular biometry for animal identification

Retinal vascular biometry was evaluated as a robust, noncontact, and noninvasive method for

unique zebrafish identification over longitudinal timepoints. A Pearson correlation coefficient

matrix was calculated by comparing vessel branch length, curvature, and angle between all

datasets. A weight averaged was then used to combine correlation matrices for each generation

into a single correlation matrix between all datasets. Here, the weighted average favored

contributions from lower generations to compensate for variability in OCT/OCT-A FOV at

longitudinal timepoints, which may result in inconsistent biometrics from higher generation vessel

branches.

Correlation coefficients in the first-generation matrix above a threshold of 0.957 were exactly

reproduced in the overall correlation coefficient matrix for the associated fish. Correlation

coefficients below 0.957 in the first generation were combined with correlation coefficients from

subsequent generations by a weighted mean method. The optimal threshold of 0.957 was

empirically determined by calculating sensitivity and specificity at various thresholds. The highest

sensitivity and specificity occurred at a threshold of 0.957 (Fig. 5).

Coefficients were assigned relative weights of 6, 7, and 4 for generations 1, 2, and 3,

respectively. A weight of 2 was assigned for coefficients in generations 4 through 6. The optimal

weight for each generation was empirically determined by calculating sensitivity and specificity

values with various weights of coefficients (1-10, sensitivity and specificity values were found to

7

increase to a maximum and then decrease within this range). Correlation coefficients for higher

generations in fish that did not have branches in these generations were excluded from weighted

mean calculations. The ten largest overall correlation coefficients for each fish were selected to

identify the images from the respective ten time points.

Fig. 5. Identification schematic. Every correlation coefficient (Corr Coef) in the

first generation (Gen 1) matrix was processed in this manner to produce the final

correlation coefficient in the overall coefficient matrix. After construction of the

overall coefficient matrix was completed, fish were matched by selecting the

highest ten (corresponding to the ten time points) correlation coefficients of each

row.

8

RESULTS

In vivo OCT and OCT-A volumes, sampled with 2048 x 500 x 2500 pix. (spectrum x A-scan x 5-

repeated B-scans), were acquired in approximately 10 s. A representative OCT dataset centered

on the ONH is presented in Figure 6 with cross-sectional retinal layers labeled based on previous

studies comparing OCT cross-sections to corresponding histology (Fig. 6(b)) [46, 47]

Fig. 6. In vivo retinal OCT in zebrafish. (a) En face OCT projection with

representative orthogonal cross-sections (blue/red lines and insets). (b) 5-frame

averaged OCT cross-section with labeled retinal layers. GCL, ganglion cell layer;

IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer;

ONL, outer nuclear layer; OS, outer segment; and RPE, retinal pigment epithelium.

The corresponding en face OCT-A projection shows central major vessels radiating outward

from the ONH (Fig. 7). OCT-A B-scans show vessel cross-sections at the surface of the retina and

flow artifacts at the RPE below each vessel. We distinguish retinal vessels from artifacts by

segmenting and isolating only the upper layer of flow signals (Fig. 7(a), arrows) because in the

adult zebrafish, the retinal vasculature forms a membranous layer that is attached to the vitreal

interface [48].

Fig. 7. In vivo retinal OCT-A in zebrafish. (a) En face OCT-A projection with

representative orthogonal cross-sections (blue/red lines and insets) showing retinal

vessels (arrow) and RPE artifacts. (b) OCT-A projection with corresponding

segmentation mask overlay. Vessel branches are color-coded based on their branch

generation relative to the ONH (white circle).

9

OCT-A vessel maps were skeletonized and the resulting vessel segments were color-coded to

represent different branches relative to the ONH (Fig. 7(b)). Any skeletonized vessel segments

that begin and end within the ONH (white circle) were ignored. Vessels that begin inside and end

outside of the ONH were considered the first branch generation (green) and branch generations

were incremented radially outward from the ONH. Total processing time for each OCT-A dataset

was approximately 13 minutes, the bulk of which was spent on volumetric registration (>12 min.).

Skeletonization and vessel segmentation was performed in ~30 s per vascular map.

Automatically segmented vessel maps were evaluated by manual graders to quantify the

robustness of our algorithm. Errors were identified in 2.5% of data (5 of 200 vessel maps) and

classified as either segmentations errors at the periphery of the FOV (Fig. 8) or inside the ONH

(Fig. 9). Figures 8 and 9 show longitudinal datasets in the same eye to highlight these segmentation

errors. At the edge of the FOV, segmentation is confounded by areas of low OCT-A contrast and

cropped vessel branches. Poor contrast results in missed branches and branch points (Fig. 8, pink

arrows). Similarly, vessels cropped at the end of the imaging FOV may result in missed branch

points (Fig. 8, orange arrows). Missing branches or branch points confound all downstream

analyses, including branch generation labeling and quantification of branch length, angle, and

curvature. Mislabeled branch generations also occurred because of overlap between a vessel

branch point and the ONH rim. In these cases, there was ambiguity in classifying the vessel

segment as either a retinal or ONH vessel, which led to the mislabeling of subsequent branch

generations originating from the initial mislabeled ONH branch (Fig. 9).

Fig. 8. Segmentation errors at the FOV periphery. Representative en face (a), (d)

OCT, (b), (e) OCT-A, and (c), (f) segmentation maps at two longitudinal

timepoints. Generation 3 branches, which are identified are misidentified as part of

the preceding generation 2 branch because of poor contrast (pink arrows).

Similarly, vessel branches cropped by the edge of the FOV with sufficient contrast

may be misidentified as part of the preceding generation (orange arrows).

10

Automatically extracted quantitative vascular biometrics were color-coded and plotted as maps

of vessel branch angle, curvature, and length to enable qualitative comparisons between

longitudinal data and eyes (Fig. 10). Biometrics were grouped by left (OS) and right (OD) eye in

each fish, and all 10 repeated longitudinal datasets in each eye were shown as vertical columns. In

the horizontal axis, branch segments (row) were grouped by branch generations. Visibly, the

biometric data becomes noisier in higher generation vessel branches because of aforementioned

segmentation errors at the edge of the FOV. Within each eye, similar biometric patterns are

observed in repeated datasets for each eye, but data between different eyes and different fish are

significantly different.

Fig. 9. Segmentation errors at the ONH. (a)-(c) En face OCT-A, (d)-(f)

segmentation maps, and (g)-(i) magnified views around the ONH at three

longitudinal timepoints. (d), (g) Correctly labeled datasets show ONH vessels in

gray. (e), (g), (f), (i) Branch generation labeling errors show ONH vessels labeled

as first-generation vessels (green).

11

Fig. 10. Quantitative vascular biometry. Vessel branch (a) angle, (b) curvature, and

(c) length of 10 repeated longitudinal datasets (columns) for both eyes of 10

zebrafish (column groups). Biometrics in each eye were grouped by branch

generation (row groups) and each row shows biometric data from each vessel in the

respective generation.

Branch vessel length was identified as the most robust biometric parameter for comparing

vascular data between longitudinal timepoints because it has the highest dynamic range and lowest

noise of all three parameters (Fig 10.). When comparing branch vessel lengths, higher order

generations (>3) were given lower weights because of increased noise as compared to the first two

generations (Fig. 11). The increased noise is attributed to differences in imaging FOV between

longitudinal timepoints that result in different higher generation branch vessels being visible (Fig.

11(b)-(d)).

12

Fig. 11. Vessel branch length comparison between longitudinal timepoints. (a)

Representative biometric map showing vessel branch length differences between

10 repeated datasets in one eye. (b)-(d) Segmented vascular maps at 3 timepoints

showing similar length parameters in the first and second vessel generations and

increased noise in higher generations as a result of FOV differences (asterisk).

As explained in the methods, the identification process uses only first generation cross-

correlation coefficients higher or equal to 0.957. This assumption is reasonable because the first

generation is more robust to the changes in images between imaging time points. FOV changes

influence first generation feature parameters slightly because they start inside the ONH and in most

cases they have branching inside the field of view. Although not all first-generation vessels branch

inside the FOV, errors in these cases are relatively small with respect to vessel length and do not

influence angle measurements at all. Curvature is influenced by such errors only if the missing

part of the vessel has a strong curve in that area. On the other hand, the dynamic range of feature

values (especially for length) is much higher than in other generations (Fig 10.). This allows us to

consider the first-generation as more robust compared to the others. Figure 12 illustrates changes

between two eyes in the first generation.

As described above we decided to weight higher the first branch generation of blood vessel

features for automatic identification based on the higher relative change in the features across fish

as compared to higher generations. Changes in higher order generations may be influenced by eye

position in the field of view (FOV) in images, leading to segmentation errors. Prior to manually

fixing these errors, a specificity of 99.82% and a sensitivity of 96.5% was achieved.

After applying the weighted algorithm to the manually corrected dataset, 1986 out of 2000

matches (20 eyes, 10 matches for each eye) were identified correctly (99.3% sensitivity). 37986

out of 38000 matches were correctly identified as mismatches (99.96% specificity). Overall, 18

out of 20 eyes matched all ten timepoints correctly. The remaining two eyes had images from two

timepoints each that did not match completely with all of the other timepoints of the associated

fish (14 images in total that did not match correctly).

13

Fig. 12. First generation length differences between eyes. (a) and (c) represent

length of each segment in the first generation of the two eyes. (b) and (d) shows

vasculature maps of those two eyes. Black arrows point out differences between

segments on the eyes. Black asterisk denote segment that appear on one eye but

not on the other.

14

DISCUSION AND CONCLUSION

A novel zebrafish identification method has been presented that is efficient and robust. Total

processing time for vascular segmentation and labeling was ~30 s, and the identification algorithm

showed a sensitivity and specificity above 99%. The algorithm correctly identified 18 out of 20

eyes in each imaging session. The proposed method addresses major limitations in large population

imaging studies of adult zebrafish, specifically the need to uniquely identify animals between

longitudinal timepoints. The ability to uniquely identify eyes with high specificity and sensitivity

reduces the need for fin cutting and housing animals in limited groups (4-5 fish) to enable

identification based on natural markings. OCT-A is demonstrated to provide functional

information to enable quantitative biometrics in zebrafish models of retinal vascular pathologies.

In the preliminary study, identification errors were primarily a result of differences in FOV

between longitudinal timepoints. Translational differences in FOV between timepoints affects the

vessel branches that are visible, which contributes to increased noise in biometric parameters for

higher order generations. Rotational differences of the FOV at different timepoints also limits the

accuracy of biometric comparisons because branch vessels may be mislabeled between datasets.

Both sources of error may be addressed using volumetric registration of images at different

timepoints at the expense of computational complexity and time. FOV differences also introduced

segmentation errors. Vessel branches that are cropped at the edge of the FOV may cause branch

points to be missed during automatic feature identification. The current algorithm defines branch

points as the point where a parent branch ramifies into two or three children branches. Loss of

children branches by FOV cropping can result in parent and a child branch to be considered as a

single vessel branch, thus resulting in significant errors in biometric quantification (Fig. 10). A

potential solution for overcoming these errors may be to omit any higher generation branch vessels

that intersect the FOV edge to avoid the potential for associated errors. ONH segmentation errors

were also observed between timepoints. Inaccurate identification of ONH vessels resulted in vessel

generation labeling errors for all subsequent branch vessels. One possible solution for ONH vessel

segmentation errors may be to appropriately threshold for first generation branches because ONH

vessels are significantly shorter than first generation vessels.

The major bottleneck of the current algorithm is volumetric registration, which was ~12 min

per volume. However, registration does not significantly limit the utility of the method because it

is performed during post-processing and therefore real-time operation is not required. In addition,

it was determined that registration of repeated OCT-A frames at the same position was not

necessary in all fish. Thus, increased OCT/OCT-A imaging speeds may obviate the need for

volumetric registration to remove bulk motion noise.

Demonstration of the use of OCT and OCT-A for structural and functional imaging of zebrafish

retina has been accomplished. Quantitative vascular biometry was extracted from automatically

segmented vessels from OCT-A and used to uniquely identify eyes in different fish between

longitudinal timepoints. Identification accuracy of 99.3% was achieved in a preliminary study of

wild-type fish over 10 repeated timepoints. The developed technology eliminates the need to

manually mark and identify animals and provides quantitative metrics for studying functional

changes in zebrafish models of retinal pathologies.

15

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